Evaluation of a singing voice conversion method based on many-to-many eigenvoice conversion

Abstract

In this paper, we evaluate our proposed singing voice conversion method from various perspectives. To enable singers to freely control their voice timbre of singing voice, we have proposed a singing voice conversion method based on many-tomany eigenvoice conversion (EVC) that enables to convert the voice timbre of an arbitrary source singer into that of another arbitrary target singer using a probabilistic model. Furthermore, to easily develop training data consisting of multiple parallel data sets between a single reference singer and many other singers, a technique for efficiently and effectively generating the parallel data sets from nonparallel singing voice data sets of many singers using a singing-to-singing synthesis system have been proposed. However, we have never conducted sufficient investigations into the effectiveness of these proposed methods. In this paper, we conduct both objective and subjective evaluations to carefully investigate the effectiveness of proposed methods. Moreover, the differences between singing voice conversion and speaking voice conversion are also analyzed. Experimental results show that our proposed method succeeds in enabling people to control their own voice timbre by using only an extremely small amount of the target singing voice.

Self-Supervised Contrastive Learning for Singing Voices

“…Similarly, we can use singing voice conversion [68], [69] to acquire feature representations attentive to vocal timbre, substituting pitch shifting. Specifically, we can generate negative anchors that alter the vocal timbre of singing voices by using singing voice conversion techniques while maintaining other aspects, such as expressive articulations.…”